Model Based Fault Detection and Diagnosis of Doubly Fed Induction Generators - A Review

Citations of this article
Mendeley users who have this article in their library.


Recent literature on fault diagnosis of Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECS) mention the importance of accuracy in diagnosis. Upgrading the existing diagnostic features in the firmware is very important to reduce the downtime of the machine. It is difficult to diagnose the mechanical faults as well as electrical faults together in the system. The various harmonic components, corresponding to different type of faults, are used to detect the faults using the Machine Current Signature Analysis (MCSA). Generally, the variables used for the diagnosis are speed, torque, vibration, current, temperature and chemical composition. Investigations on the effectiveness of the model based diagnostic techniques for the DFIG based WECS has been progressing over the last two decades. A comprehensive list of modeling methodologies for the DFIG, is discussed in this paper. These are i) qd0 transformation based method, ii) Finite Element Method (FEM), iii) Magnetic Equivalent Circuit (MEC) method, and iv) Winding Function Analysis (WFA) based method. In particular, a bird's eye view of different types of modeling techniques helps the researcher in incipient fault modeling of the DFIG. Choosing the best modeling method depends on the application. In addition, the researcher has to improve the diagnostic properties such as the accuracy and reliability with respect to parametric variation and environmental factors.




Balasubramanian, A., & Muthu, R. (2017). Model Based Fault Detection and Diagnosis of Doubly Fed Induction Generators - A Review. In Energy Procedia (Vol. 117, pp. 935–942). Elsevier Ltd.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free